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Data Compression

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/ethz-asl/segmap

공식 코드

SegMap: 3D Segment Mapping using Data-Driven Descriptors

https://github.com/eyalfish/artoveq

공식 코드

Remote Inference over Dynamic Links via Adaptive Rate Deep Task-Oriented Vector Quantization

https://github.com/facebookresearch/qinco

공식 코드

Residual Quantization with Implicit Neural Codebooks

https://github.com/facebookresearch/zest

공식 코드

Text Ranking and Classification using Data Compression

https://github.com/fan-aaron-zhang/bvi-cr

공식 코드

BVI-CR: A Multi-View Human Dataset for Volumetric Video Compression

https://github.com/fraunhoferhhi/Self-Organizing-Gaussians

공식 코드

Compact 3D Scene Representation via Self-Organizing Gaussian Grids

https://github.com/fuhengzhou/lsnet

공식 코드

A 7K Parameter Model for Underwater Image Enhancement based on Transmission Map Prior

https://github.com/gericko/grouprandomizedresponse

공식 코드

Communication Cost Reduction for Subgraph Counting under Local Differential Privacy via Hash Functions

https://github.com/giovanniseraghiti/relu-nmd

공식 코드

An extrapolated and provably convergent algorithm for nonlinear matrix decomposition with the ReLU function

https://github.com/google-research/google-research

공식 코드

Tackling Provably Hard Representative Selection via Graph Neural Networks

https://github.com/granica-ai/lossycompressionscalingkdd2024

공식 코드

Scaling Training Data with Lossy Image Compression

https://github.com/havelhuang/hda-testing

공식 코드

Hierarchical Distribution-Aware Testing of Deep Learning

https://github.com/hesfahanizadeh/neuralinformationshaping

공식 코드

TexShape: Information Theoretic Sentence Embedding for Language Models

https://github.com/hmrishavbandy/SketchINR-Code

공식 코드

SketchINR: A First Look into Sketches as Implicit Neural Representations

https://github.com/hudsonchen/MMDF_cubature

공식 코드

Stationary MMD Points for Cubature

https://github.com/hulei7/replaycad

공식 코드

ReplayCAD: Generative Diffusion Replay for Continual Anomaly Detection

https://github.com/idea-iitd/mirage

공식 코드

Mirage: Model-Agnostic Graph Distillation for Graph Classification

https://github.com/ika-rwth-aachen/point-cloud-compression

공식 코드

3D Point Cloud Compression with Recurrent Neural Network and Image Compression Methods

https://github.com/ioangatop/srVAE

공식 코드

Self-Supervised Variational Auto-Encoders

https://github.com/jinsian/COMET

공식 코드

COMET: A Novel Memory-Efficient Deep Learning Training Framework by Using Error-Bounded Lossy Compression

https://github.com/jsford64/jiffy-compression

공식 코드

Lossless SIMD Compression of LiDAR Range and Attribute Scan Sequences

https://github.com/juice-jl/pressedjuice.jl

공식 코드

Lossless Compression with Probabilistic Circuits

https://github.com/justincui03/dc_benchmark

공식 코드

DC-BENCH: Dataset Condensation Benchmark

https://github.com/kagaminccino/LAVSE

공식 코드

Lite Audio-Visual Speech Enhancement

https://github.com/kai-yue/predictive-coding-fl

공식 코드

Communication-Efficient Federated Learning via Predictive Coding

https://github.com/kensho-technologies/pathpiece

공식 코드

Tokenization Is More Than Compression

https://github.com/kensho-technologies/timtc_vocabs_models

공식 코드

Tokenization Is More Than Compression

https://github.com/kokikwbt/ssmf

공식 코드

SSMF: Shifting Seasonal Matrix Factorization

https://github.com/kotanakm/cubescope

공식 코드

Fast and Multi-aspect Mining of Complex Time-stamped Event Streams

https://github.com/kvndhrty/quadconv

공식 코드

QuadConv: Quadrature-Based Convolutions with Applications to Non-Uniform PDE Data Compression

https://github.com/kylehkhsu/tripod

공식 코드

Tripod: Three Complementary Inductive Biases for Disentangled Representation Learning

https://github.com/leieric/nerd-rcc

공식 코드

Neural Estimation of the Rate-Distortion Function With Applications to Operational Source Coding

https://github.com/lh3/ropebwt3

공식 코드

BWT construction and search at the terabase scale

https://github.com/liaoliang2020/talgebra

공식 코드

Generalized Image Reconstruction over T-Algebra

https://github.com/liyucheng09/llm-compressive

공식 코드

Evaluating Large Language Models for Generalization and Robustness via Data Compression

https://github.com/louisyulu/veob-and-st

공식 코드

Fast Machine Learning Method with Vector Embedding on Orthonormal Basis and Spectral Transform

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.